In this paper, we propose a new approach for identifying the language type of character images. We do this by classifying individual character images to determine the language boundaries in multilingual documents. Two effective methods are considered for this purpose: the prototype classification method and support vector machines (SVM). Due to the large size of our training dataset, we further propose a technique to speed up the training process for both methods. Applying the two methods to classifying characters into Chinese, English, and Japanese (including Hiragana and Katakana) has produced very accurate and comparable test results.